Instructions to use google/tapas-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="google/tapas-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("google/tapas-large") model = AutoModel.from_pretrained("google/tapas-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-large: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/google/tapas-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-large/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- d7dae780d4505df969320fe3b16bb2fdcb1914f22c00f762f3e6e31b034943cd
- Size of remote file:
- 1.35 GB
- SHA256:
- 6a9a9a572565744c92412fec445816803630901d589b87b531233212f8bdd47b
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